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  • PyCaret 3.0
  • GET STARTED
    • 💻Installation
    • 🚀Quickstart
    • ⭐Tutorials
    • 📶Modules
    • ⚙️Data Preprocessing
      • Data Preparation
      • Scale and Transform
      • Feature Engineering
      • Feature Selection
      • Other setup parameters
    • 💡Functions
      • Initialize
      • Train
      • Optimize
      • Analyze
      • Deploy
      • Others
  • LEARN PYCARET
    • 📖Blog
      • Announcing PyCaret 1.0
      • Announcing PyCaret 2.0
      • 5 things you dont know about PyCaret
      • Build and deploy your first machine learning web app
      • Build your own AutoML in Power BI using PyCaret
      • Deploy ML Pipeline on Google Kubernetes
      • Deploy PyCaret and Streamlit on AWS Fargate
      • Anomaly Detector in Power BI using PyCaret
      • Deploy ML App on Google Kubernetes
      • Deploy Machine Learning Pipeline on GKE
      • Deploy Machine Learning Pipeline on AWS Fargate
      • Deploy ML Pipeline on the cloud with Docker
      • Clustering Analysis in Power BI using PyCaret
      • Deploy PyCaret Models on edge with ONNX Runtime
      • GitHub is the best AutoML you will ever need
      • Deploy PyCaret and Streamlit on AWS Fargate
      • Easy MLOps with PyCaret and MLflow
      • Clustering Analysis in Power BI using PyCaret
      • Machine Learning in Alteryx with PyCaret
      • Machine Learning in KNIME with PyCaret
      • Machine Learning in SQL using PyCaret Part I
      • Machine Learning in Power BI using PyCaret
      • Machine Learning in Tableau with PyCaret
      • Multiple Time Series Forecasting with PyCaret
      • Predict Customer Churn using PyCaret
      • Predict Lead Score (the Right Way) Using PyCaret
      • NLP Text Classification in Python using PyCaret
      • Predict Lead Score (the Right Way) Using PyCaret
      • Predicting Crashes in Gold Prices Using PyCaret
      • Predicting Gold Prices Using Machine Learning
      • PyCaret 2.1 Feature Summary
      • Ship ML Models to SQL Server using PyCaret
      • Supercharge Your ML with PyCaret and Gradio
      • Time Series 101 - For beginners
      • Time Series Anomaly Detection with PyCaret
      • Time Series Forecasting with PyCaret Regression
      • Topic Modeling in Power BI using PyCaret
      • Write and train custom ML models using PyCaret
      • Build and deploy ML app with PyCaret and Streamlit
      • PyCaret 2.3.6 is Here! Learn What’s New?
    • 📺Videos
    • 🛩️Cheat sheet
    • ❓FAQs
    • 👩‍💻Examples
  • IMPORTANT LINKS
    • 🛠️Release Notes
    • ⚙️API Reference
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On this page
  • Option 1: Install via PyPi
  • Option 2: Source
  • Option 3: Docker
  • Environment
  • Training on GPU
  • PyCaret Intel sklearnex support

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  1. GET STARTED

Installation

A step-by-step guide to install PyCaret in Python

Option 1: Install via PyPi

PyCaret is tested and supported on 64-bit systems with:

  • Python 3.8, 3.9, 3.10, and 3.11

  • Ubuntu 16.04 or later

  • Windows 7 or later

You can install PyCaret with Python's pip package manager:

pip install pycaret

PyCaret's default installation will not install all the optional dependencies automatically. Depending on the use case, you may be interested in one or more extras:

# install analysis extras
pip install pycaret[analysis]

# models extras
pip install pycaret[models]

# install tuner extras
pip install pycaret[tuner]

# install mlops extras
pip install pycaret[mlops]

# install parallel extras
pip install pycaret[parallel]

# install test extras
pip install pycaret[test]

## 

# install multiple extras together
pip install pycaret[analysis,models]
# install full version
pip install pycaret[full]

Option 2: Source

Install the development version of the library directly from the source. The API may be unstable. It is not recommended for production use.

pip install git+https://github.com/pycaret/pycaret.git@master --upgrade

Option 3: Docker

Docker creates virtual environments with containers that keep a PyCaret installation separate from the rest of the system. PyCaret docker comes pre-installed with a Jupyter notebook. It can share resources with its host machine (access directories, use the GPU, connect to the Internet, etc.). The PyCaret Docker images are always tested for the latest major releases.

# default version
docker run -p 8888:8888 pycaret/slim

# full version
docker run -p 8888:8888 pycaret/full

Environment

# create a conda environment
conda create --name yourenvname python=3.8

# activate conda environment
conda activate yourenvname

# install pycaret
pip install pycaret

# create notebook kernel
python -m ipykernel install --user --name yourenvname --display-name "display-name"

Training on GPU

To train models on the GPU, simply pass use_gpu = True in the setup function. There is no change in the use of the API; however, in some cases, additional libraries have to be installed. The following models can be trained on GPUs:

  • Extreme Gradient Boosting

  • Catboost

PyCaret Intel sklearnex support

pip install scikit-learn-intelex
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Last updated 1 year ago

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Check out all . If you want to install everything including all the optional dependencies:

To learn more, check out the Docker page for or .

In order to avoid potential conflicts with other packages, it is strongly recommended to use a virtual environment, e.g. python3 virtualenv (see ) or . Using an isolated environment makes it possible to install a specific version of pycaret and its dependencies independently of any previously installed Python packages.

Light Gradient Boosting Machine requires

Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requires

You can apply for machine learning algorithms and speed up your workflows. To train models with Intel optimizations use sklearnex engine. There is no change in the use of the API, however, installation of Intel sklearnex is required:

💻
optional dependencies
pycaret/slim
pycaret/full
python3 virtualenv documentation
conda environments
GPU specific installation
cuML >= 0.15
Intel optimizations